1. CS273A Lecture 2: Protein Coding Genes
MW  1:30-2:50pm in Clark S361* (behind Peet’s)
Profs: Serafim Batzoglou & Gill Bejerano
CAs: Karthik Jagadeesh & Johannes Birgmeier
* Handful of lectures/primers elsewhere: track on website/piazza
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2. Announcements http://cs273a.stanford.edu/
Course guidelines, office hours, etc.
Lecture 1 is posted
Problem set 1 rolls out next week
Course communications via Piazza
Auditors please sign up too
The first tutorial this Friday in Beckman B-302 from 2:00pm-3:30pm. It's the only one some students should consider skipping. While they may be familiar with the first half of the Molecular Biology 101 lecture, we also cover gene regulation and genome rearrangements.
CAs will be sending out a Doodle poll via Piazza to identify ideal times for office hours. Students can contact them via Piazza for questions.
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3. Class Goals Meet your genome (learn to surf, learn the surf)
Understand genomic tools (theory, applications)
DIY (pose questions, write & run tools, understand answers)
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4. Class Topics (0) Genome context: cells, DNA, central dogma
(1) Genome content / genome function: genes, gene regulation, repeats, epigenetics
(2) Genome sequencing: technologies, assembly/analysis, technology dependence
(3) Genome evolution: evolution = mutation + selection, modes of evolution, comparative genomics, ultraconservation, exaptation
(4) Population genomics: Tracking human migration patterns via neutral evolution
(5) Genomics of human disease: disease susceptibility, cancer genomics, personal genomics
(6) Genome “output” (organism) evolution: Evolutionary developmental biology (“evo-devo”)
[BejeranoFall15/16]

5. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT
Genome Context
Our ultimate goal is to systematically identify all functional elements in the human genome.
Namely, the ability of transforming raw DNA sequence into a fully annotated genome.
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6. Organism – Cell - Genome
1013 different cells in an adult human. The cell is the basic unit of life.
DNA = linear molecule inside the cell that carries instructions needed throughout the cell’s life ~ long string(s) over a small alphabet
Alphabet (nucleotides/bases) {A,C,G,T}
Strings (chromosomes) of length
“instruction”
Genome:
...ACGTACGACTGACTAGCATCGACTACGACTAGCAC...
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7. One Cell, One Genome, One Replication
Every cell holds a copy of all its DNA = its genome.
The human body is made of ~1013 cells.
All originate from a single cell through repeated cell divisions.
egg
DNA strings =
Chromosomes
egg
cell
cell
division
genome =
all DNA
chicken
egg
chicken ≈ 1013 copies (DNA) of egg (DNA)
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8. What will we study?
The most amazing “Turing tape” in existence, your genome.
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9. How to Read The Genome Genome = DNA.
Genome is broken up into several strings = chromosomes.
Humans: Females= (2*chr.1-22)+XX Males= (2*chr.1-22)+XY
DNA is double stranded.
Complementation is rigid.
Information can be read off of either strand.
DNA strings =
Chromosomes
cell
cell
division
genome =
all DNA
Every cell contains 2 copies of your genome, one from mom, one from dad.
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10. The Biggest Challenge in Genomics…
… is computational: How does this encode this
Program
Output
This “coding” question has profound implications for our lives
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11. Class Topics (0) Genome context: cells, DNA, central dogma
(1) Genome content / genome function: genes, gene regulation, repeats, epigenetics
(2) Genome sequencing: technologies, assembly/analysis, technology dependence
(3) Genome evolution: evolution = mutation + selection, modes of evolution, comparative genomics, ultraconservation, exaptation
(4) Population genomics: Tracking human migration patterns via neutral evolution
(5) Genomics of human disease: disease susceptibility, cancer genomics, personal genomics
(6) Genome “output” (organism) evolution: Evolutionary developmental biology (“evo-devo”)
[BejeranoFall15/16]

12. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG
Genome Content
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13. Genomes, Genes & Proteins
The most visible instructions in our genome are Genes.
Genes explain exactly HOW to synthesize any protein.
Proteins are the work horses of every living cell.
gene
Genome:
...ACGTACGACTGACTAGCATCGACTACGACTAGCAC...
linear (folded) molecule
protein
cell
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14. Central Dogma of Biology
genome
{A,C,G,T}
{A,C,G,U}
next page
{20 letters}

15. Translation: The Genetic Code
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16. Genes Can Be Encoded on Either Strand
Watson strand
Crick strand
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17. Gene Structure
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18. Gene Splicing
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19. Visualizing Gene Structure
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20. Genes in the Human Genome
UCSC primer
There are ~20,000 protein coding genes in the human genome.
(Even half way through sequencing the human genome, Researchers thought there will be well over 100,000 genes).
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21. Gene Finding Computational Challenge:
“Find the genes, the whole genes, and nothing but the genes”
Understand Biology  Write discovery tools
(Our) answer depends on our understanding, data & tools
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22. Gene prediction approachs
Rule-based programs
Use explicit set of rules to make decisions.
Example: GeneFinder
Neural Network-based programs
Use data set to build rules.
Examples: Grail, GrailEXP
Hidden Markov Model-based programs
Use probabilities of states and transitions between these states to predict features.
Examples: Genscan, GenomeScan

23. GenScan States N - intergenic region P - promoter
F - 5’ untranslated region
Esngl – single exon (intronless) (translation start -> stop codon)
Einit – initial exon (translation start -> donor splice site)
Ek – phase k internal exon (acceptor splice site -> donor splice site)
Eterm – terminal exon (acceptor splice site -> stop codon)
Ik – phase k intron: 0 – between codons; 1 – after the first base of a codon; 2 – after the second base of a codon

24. Alternative Splicing
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25. Genes in the Human Genome
When you only show one transcript per gene locus:
If you ask the GUI to show you all well established gene variants:
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26. Protein Domains
SKSHSEAGSAFIQTQQLHAAMADTFLEHMCRLDIDSAPITARNTGIICTIGPASRSVETLKEMIKSGMNVARMNFSHGTHEYHAETIKNVRTATESFASDPILYRPVAVALDTKGPEIRTGLIKGSGTAEVELKKGATLKITLDNAYMAACDENILWLDYKNICKVVEVGSKVYVDDGLISLQVKQKGPDFLVTEVENGGFLGSKKGVNLPGAAVDLPAVSEKDIQDLKFGVDEDVDMVFASFIRKAADVHEVRKILGEKGKNIKIISKIENHEGVRRFDEILEASDGIMVARGDLGIEIPAEKVFLAQKMIIGRCNRAGKPVICATQMLESMIKKPRPTRAEGSDVANAVLDGADCIMLSGETAKGDYPLEAVRMQHLIAREAEAAMFHRKLFEELARSSSHSTDLMEAMAMGSVEASYKCLAAALIVLTESGRSAHQVARYRPRAPIIAVTRNHQTARQAHLYRGIFPVVCKDPVQEAWAEDVDLRVNLAMNVGKAAGFFKKGDVVIVLTGWRPGSGFTNTMRVVPVP
A protein domain is a subsequence of the protein that folds independently of the other portions of the sequence, and often confers to the protein one or more specific functions.
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27. Alt. Splicing and Protein Repertoire
Alternative splicing often produces protein variants that have a different domain composition, and thus perform different functions.
What if we want to predict all splice variants that are ever made? Can we even do it from sequence alone?
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28. Common Problems Common problems with gene finders Other challenges
Fusing neighboring genes
Spliting a single gene
Miss exons or entire genes
Overpredict exons or genes
Other challenges
Nested genes
Noncanonical splice spites
Pseudogenes
Different isoforms of same gene

29. We can sequence all mRNA of a given cell
(Great, but not all genes/isoforms are expressed in all cells. Some are very exotic).
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30. Gene Annotation System
All Ensembl gene predictions are based on experimental evidence
Predictions based on manually curated Uniprot/Swissprot/Refseq databases
UTRs are annotated only if they are supported by EMBL mRNA records
Val Curwen, et al. The Ensembl Automatic Gene Annotation System Genome Res., (2004)

31. First full draft of the Human Genome
Human Genome Consortium
(HGC)
Celera
2001
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32. Everything in Genomics is a Moving Target
The genomes (ie, assemblies)
Their annotations
Our understanding of Biology
The portals
Conclusion:
write code
that can be
run...
and rerun

33. Biological Functions of the Human Gene Set
Focus on the X axis:
[HGC, 2001]
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34. Molecular Functions of the Human Gene Set
[Celera, 2001]
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35. Biological vs. Molecular Function: Pathways
Proteins with very different molecular functions participate to manifest a single biological function, for example: a pathway.
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36. Gene Sets Gene Ontology (“GO”) Pathway Databases Multiple others
Biological Process
Molecular Function
Cellular Location
Pathway Databases
KEGG
BioCarta
Broad Institute
Multiple others

37. Genes & Their Functions
Gene (DNA) sequence determines protein (AA) sequence, which determines protein (3D) structure, which determines protein’s function.
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38. Protein Folding
Protein folding is the challenge of deducing protein structure from protein sequence.
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39. Gene Families, Gene Names
Genes (proteins) come in families.
Genes of the same family have similar sequences.
Which is why the fold into similar structure and perform similar functions.
Genes of the same family will typically have a “family name” followed by a (sequential) number or “first name”.
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40. Some “Special” Functions: Gene Regulation
2,000 different proteins can bind specific DNA sequences.
DNA
Proteins
Protein binding site
Gene
DNA
Proteins that regulate the transcription of other proteins are called transcription factors.
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41. The Importance of Gene Regulation
The looks & capabilities of different cells are determined by the subset of genes they express.
Different cell types express very different gene repertoires (from the same genome).
To change its behavior a cell can change its transcriptional program.
Think of it as a giant state machine…
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42. “Special” Function: Cell Signaling
Cells also talk with each other. They send and receive messages, and change their behavior according to messages they receive.
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43. Biological vs. Molecular Function: Pathways
Proteins with very different molecular functions participate to manifest a single biological function, for example: a pathway.
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44. Signal Transduction
Now its an even bigger state machine of individual state machines (=cells) talking with each other, orchestrating their individual activities.
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45. http://cs273a.stanford.edu [BejeranoFall15/16]